Evolving graphs: dynamical models, inverse problems and propagation
Evolving graphs: dynamical models, inverse problems and propagation
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DOI：
10.1098/rspa.2009.0456
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发表时间：
2010-03-08
影响因子：
3.5
通讯作者：
Higham, Desmond J.
中科院分区：
文献类型：
--
作者：
Grindrod, Peter;Higham, Desmond J.
神经科学、电信、在线社交网络、运输和零售贸易等应用产生了随时间变化的连接模式。在这项工作中，我们解决了由此产生的网络模型和计算算法，处理动态链接的需要。我们引入了一类新的不断发展的范围依赖随机图，给出了一个易于处理的框架建模和仿真。我们开发了一种谱算法，用于从一系列网络快照中校准一组边缘范围，并在一些神经科学数据上给出了原理说明的证明。我们还展示了如何使用该模型进行计算和分析，以调查的情况下，一个进化的过程，如流行病，发生在一个不断发展的网络。这使我们能够研究两种不同类型的动态的累积效应。
Applications such as neuroscience, telecommunication, online social networking, transport and retail trading give rise to connectivity patterns that change over time. In this work, we address the resulting need for network models and computational algorithms that deal with dynamic links. We introduce a new class of evolving range-dependent random graphs that gives a tractable framework for modelling and simulation. We develop a spectral algorithm for calibrating a set of edge ranges from a sequence of network snapshots and give a proof of principle illustration on some neuroscience data. We also show how the model can be used computationally and analytically to investigate the scenario where an evolutionary process, such as an epidemic, takes place on an evolving network. This allows us to study the cumulative effect of two distinct types of dynamics.